98%
921
2 minutes
20
Objectives: To analyze the multimorbidity patterns and core diseases among hospitalized patients in different age groups and to explore the impacts of multimorbidity patterns on hospitalization costs.
Methods: Electronic medical records of adult inpatients (aged ≥18 years) from Ningbo Medical Center Lihuili Hospital between January 1, 2018, and June 30, 2023 were collected. The multimorbidity status involving 53 specific diseases was analyzed across different age groups. Association rule mining was used to identify common multimorbidity patterns. Complex network analysis was used to identify core diseases within the multimorbidity networks. Generalized estimating equations (GEE) were used to analyze the impact of different multimorbidity patterns on hospitalization costs.
Results: The prevalence of multimorbidity among the 359 402 adult inpatients was 38.51%, with higher rates observed in males (43.60%) and elderly patients (58.29%). Association rule mining identified 15 common multimorbidity patterns, which exhibited differences across age groups. The most prevalent multimorbidity pattern overall was "diabetes→hypertension" (support=7.04%, confidence=62.17%, lift=2.17). In the young adult group, the most prevalent pattern was "dyslipidemia→chronic liver disease" (support=1.19%, confidence=53.17%, lift=6.04). In the middle-aged group, it was "diabetes→hypertension" (support=4.84%, confidence=50.28%, lift=2.15). In the elderly group, it was "coronary heart disease, diabetes→hypertension" (support=2.38%, confidence=77.43%, lift=1.63). Complex network analysis revealed that the core diseases within multimorbidity networks differed across age groups. The core disease identified in the young adult group was chronic liver disease (degree centrality=50, betweenness centrality=0.055, closeness centrality=0.963). Core diseases in the middle-aged group included hypertension, chronic liver disease, and diabetes (all with degree centrality=52, betweenness centrality=0.022, closeness centrality=1.000). Core diseases in the elderly group comprised hypertension, diabetes, malignant tumors, chronic liver disease, thyroid disease, anemia, and arrhythmia (all with degree centrality=52, betweenness centrality=0.009, closeness centrality=1.000). Generalized estimating equations analysis indicated that, most multimorbidity patterns were significantly associated with increased hospitalization costs. However, the magnitude of cost increase varied across different multimorbidity patterns. Specifically, hospitalization costs for patients with patterns such as "heart failure→hypertension", "stroke→hypertension", "malignant tumor, diabetes→hypertension", "stroke, diabetes→hypertension", and "diabetes, heart failure→hypertension" were more than double those of patients without any target diseases.
Conclusions: Multimorbidity patterns and core diseases among hospitalized patients differ significantly across age groups, and different patterns exert varying impacts on hospitalization costs. These findings underscore the necessity for age-stratified and multimorbidity pattern specific management strategies.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12382317 | PMC |
http://dx.doi.org/10.3724/zdxbyxb-2025-0054 | DOI Listing |
Diabetes Obes Metab
September 2025
Phase I Clinical Trial Research Ward, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, People's Republic of China.
Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is an emerging global health concern, and its presence increases the risk of multi-system diseases. This study aimed to investigate the multimorbidity trajectories of chronic diseases in people living with MASLD.
Methods: We identified 137 859 MASLD patients in UK Biobank and used 'propensity score matching' to match an equal number of non-MASLD controls.
Front Med (Lausanne)
August 2025
Chongqing General Hospital, Chongqing, China.
Background: The prevalence, patterns, and impact of multimorbidity on health-related quality of life (HRQoL) remain inadequately understood among rural populations in southwest China. This study seeks to fill this knowledge gap by systematically examining these aspects.
Methods: Participants were recruited from the China Multi-Ethnic Cohort (CMEC) study.
Compr Physiol
October 2025
School of Pharmacy and Medical Sciences, Griffith University, Southport, Queensland, Australia.
Mechanisms underlying cardiovascular, affective, and metabolic (CAM) multimorbidity are incompletely defined. We assessed how two risk factors-chronic stress (CS) and a Western diet (WD)-interact to influence cardiovascular function, resilience, adaptability, and allostatic load (AL); explore pathway involvement; and examine relationships with behavioral, metabolic, and systemic AL. Male C57Bl/6 mice (8 weeks old, n = 64) consumed a control (CD) or WD (12%-65%-23% or 32%-57%-11% calories from fat-carbohydrate-protein) for 17 weeks, with half subjected to 2 h daily restraint stress over the final 2 weeks (CD + CS and WD + CS).
View Article and Find Full Text PDFTher Adv Respir Dis
September 2025
Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu 610041, China.
Background: Hermansky-Pudlak syndrome (HPS) is a rare disease characterized by excessive bleeding, oculocutaneous albinism, and pulmonary fibrosis (PF). However, few studies have systematically summarized the clinical characteristics of HPS.
Objectives: To summarize the clinical characteristics, risk factors of PF, radiological and pathological presentations, and prognostic factors in patients with HPS.
STAR Protoc
September 2025
Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen 9700 RB, the Netherlands. Electronic address:
Research on multimorbidity patterns promotes our understanding of the common pathological mechanisms that underlie co-occurring diseases. Here, we present a protocol to infer multimorbidity clusters in the form of disease topics from large-scale diagnosis data using treeLFA, a topic model based on the Bayesian binary non-negative matrix factorization. We describe steps for installing software, preparing input data, and training the model.
View Article and Find Full Text PDF